Bibliometrix Analysis using R
Bibliometrix (https://www.bibliometrix.org/) allows R users to import a bibliography database generated using SCOPUS and Web of Science stored either as a Bibtex (.bib) or Plain Text (.txt) file.
The package has simple functions which allows for descriptive analyses as shown in table-1 to table-3.
The analysis can also be easily visualised as shown in figure-1.
library(bibliometrix) #load the package
library(pander)#other required packages
library(knitr)
library(kableExtra)
library(ggplot2)
library(bibliometrixData)
#use scopuscollection data from the package
# Manuscripts including the term "bibliometrics" in the title.
# Period: 1975 - 2017
# Database: SCOPUS
# Format: bibtex
data("scopusCollection")
file1=data("scopusCollection")
#M=convert2df(file="insert filename",format="bibtex",dbsource = "scopus")#convert the data to data frame
#scopusCollection=convert2df(file="scopus.bib",dbsource = "scopus",format="bibtex")
Descriptive Analysis
Productive Authors
--------------------------------------------
Description Results
-------------------------------- -----------
MAIN INFORMATION ABOUT DATA
Timespan 1975:2017
Sources (Journals, Books, etc) 280
Documents 487
Average years from publication 13.6
Average citations per 10.36
documents
Average citations per year per 0.6601
doc
References 12245
DOCUMENT TYPES
article 417
book 12
conference 58
DOCUMENT CONTENTS
Keywords Plus (ID) 1436
Author's Keywords (DE) 722
AUTHORS
Authors 949
Author Appearances 1187
Authors of single-authored 162
documents
Authors of multi-authored 787
documents
AUTHORS COLLABORATION
Single-authored documents 184
Documents per Author 0.513
Authors per Document 1.95
Co-Authors per Documents 2.44
Collaboration Index 2.6
--------------------------------------------
Table: Summary Information
Most cited papers
-----------------------------------------------------------------
Authors Articles Authors Articles Fractionalized
--------------- ---------- ------------ -------------------------
BORNMANN L 13 BORNMANN L 6.75
KOSTOFF RN 8 HOLDEN G 4.25
GLNZEL W 7 WHITE HD 4.00
HOLDEN G 7 MARX W 3.42
MARX W 7 ATKINSON R 3.00
HUANG L 5 NA 3.00
HUMENIK JA 5 GLNZEL W 2.67
LARIVIRE V 5 KIRBY A 2.50
LEYDESDORFF L 5 PERITZ BC 2.50
ZHANG X 5 SMITH DR 2.50
-----------------------------------------------------------------
Table: Most Productive Authors
Information Plots
Summary Plot-1 (Most Porductive Authors)





Summary Plot-2 (Most Productive Countries)

Summary Plot-3 (Annual Scientific Production)

Summary Plot-4 (Average Article Citation)


- The package also facilitates various network analysis like, co-citation analysis, coupling analysis, collaboration analysis or co-occurrence analysis. Figure-2 shows a key word co-occurrence plot

- Bibliometrix provides another useful function to plot a Sankey diagram to visualise multiple attributes at the same time. For example, figure-9 provides a three fields plot for Author, Author Keywords and Cited References.

Co-word Analysis
- Analysis of the conceptual structure among the articles analysed.
- Bibliomentrix can conduct a co-word analysis to map the conceptual structure of a framework using the word co-occurrences in a bibliographic database.
- The analysis in Figure-2 is conducted using the Correspondence Analysis and K-Means clustering using Author’s keywords. This analysis includes Natural Language Processing and is conducted without stemming.
Author collaboration network
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Thematic Map
Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.
Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.
#Map2=thematicEvolution(M3,field="ID",n=1000,stemming=FALSE,repel=TRUE,years=2000)
Map=thematicMap(M, field = "ID", n = 1000, minfreq = 5,stemming = FALSE, size = 0.5, n.labels=4, repel = TRUE)
plot(Map$map)

There is a gui too!
This concludes the example. There are various online sources to take this further
---
title: "Systematic Literature Review"
author: "Abhay Singh"
date: "`r format(Sys.time(), '%d %B, %Y')`"
number_sections: true
output:
  html_document:
    df_print: paged
  bookdown::word_document2:
    toc: true
  pdf_document: default
  word_document: default
  html_notebook: default
editor_options:
  chunk_output_type: inline
  fig_caption: true
---

```{r include=FALSE}
library(knitr)
opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE,fig.env="figure",message=FALSE,warning=FALSE)
options(tidy.opts=list(keep.blank.line=TRUE,width.cutoff=60), width=55,out.width='10cm',out.height='10cm',breaklines=TRUE,fig.widht=8,fig.height=8)
```


# Bibliometrix Analysis using R

* Bibliometrix (https://www.bibliometrix.org/) allows R users to import a bibliography database generated using SCOPUS and Web of Science stored either as a Bibtex (.bib) or Plain Text (.txt) file.

* The package has simple functions which allows for descriptive analyses as shown in table-1 to table-3.

* The analysis can also be easily visualised as shown in figure-1.

```{r,eval=TRUE,echo=TRUE}
library(bibliometrix) #load the package
library(pander)#other required packages
library(knitr)
library(kableExtra)
library(ggplot2)
library(bibliometrixData)
#use scopuscollection data from the package
# Manuscripts including the term "bibliometrics" in the title.
# Period: 1975 - 2017
# Database: SCOPUS
# Format: bibtex
data("scopusCollection")
file1=data("scopusCollection")


#M=convert2df(file="insert filename",format="bibtex",dbsource = "scopus")#convert the data to data frame

#scopusCollection=convert2df(file="scopus.bib",dbsource = "scopus",format="bibtex")
```
## Descriptive Analysis

<!-- ```{r} -->
<!-- #print("seminar at Ecu") -->
<!-- ``` -->



```{r, TRUE}
#Descriptive analysis 
M=scopusCollection #just to reuse the other code
res1=biblioAnalysis(M, sep=";")
s1=summary(res1,k=10,pause=FALSE,verbose=FALSE)

d1=s1$MainInformationDF #main information 
d2=s1$MostProdAuthors #Most productive Authors 
d3=s1$MostCitedPapers #most cited papers 
pander(d1,caption="Summary Information") 
```

## Productive Authors

```{r}
s1$MostProdAuthors
pander(d2,caption="Most Productive Authors",table.split=Inf) 

```


## Most cited papers

```{r}
pander(d3,caption="Most Cited Papers") 

```

## Information Plots

```{r,eval=TRUE,results="hide",fig.show='hide'}
p1=plot(res1,pause=FALSE)
```
## Summary Plot-1 (Most Porductive Authors)

```{r}
library(ggplot2)
theme_set(theme_bw())


p1[[1]]+theme_bw()+scale_x_discrete(limits =rev(levels(as.factor(p1[[1]]$data$AU))))
```
## Summary Plot-2 (Most Productive Countries)

```{r,fig.cap="Most Productive Authors"}
p1[[2]]
```

## Summary Plot-3 (Annual Scientific Production)

```{r}
p1[[3]]
```
## Summary Plot-4 (Average Article Citation)

```{r}
p1[[4]]
```
* A graph for author statistics over time can also be produced.

* Figure-1 shows a graph of top 10 authors over time. The information from these plots can be easily extracted to summarise them in a table.

```{r fig.width=10}
topAU=authorProdOverTime(M,k=10,graph=TRUE)

```

* The package also facilitates various network analysis like, co-citation analysis, coupling analysis, collaboration analysis or co-occurrence analysis. Figure-2 shows a key word co-occurrence plot
```{r,fig.cap='Country Collaboration'}

M <- metaTagExtraction(M, Field = "AU_CO", sep = ";") 
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
# Plot the network 
net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.7,cluster="none")
```

* Bibliometrix provides another useful function to plot a Sankey diagram to visualise multiple attributes at the same time. For example, figure-9 provides a three fields plot for Author, Author Keywords and Cited References.

```{r fig.height=12, fig.width=20,,out.width="25cm",out.height="20cm"}
threeFieldsPlot(M, fields=c("DE","AU","CR")) 

```

## Co-word Analysis

* Analysis of the conceptual structure among the articles analysed. 
* Bibliomentrix can conduct a co-word analysis to map the conceptual structure of a framework using the word co-occurrences in a bibliographic database. 
* The analysis in Figure-2 is conducted using the Correspondence Analysis and K-Means clustering using Author's keywords. This analysis includes Natural Language Processing and is conducted without stemming.

```{r, fig.cap='Conceptual Structure',fig.width=15,fig.height=15}
library(gridExtra)
CS=conceptualStructure(M,field="DE", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10,graph=FALSE) 

grid.arrange(CS[[4]],CS[[5]],CS[[6]],CS[[7]],ncol=2,nrow=2)

```

## Author collaboration network

```{r,fig.width=8,fig.height=8}
NetMatrix <- biblioNetwork(M, analysis = "collaboration",  network = "authors", sep = ";")
net=networkPlot(NetMatrix,  n = 20, Title = "Author collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=0.6)
```

# Thematic Map

Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.

Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.





```{r ThematicMap, echo=TRUE, fig.height=9, fig.width=9}
#Map2=thematicEvolution(M3,field="ID",n=1000,stemming=FALSE,repel=TRUE,years=2000)
Map=thematicMap(M, field = "ID", n = 1000, minfreq = 5,stemming = FALSE, size = 0.5, n.labels=4, repel = TRUE)
plot(Map$map)
```

# There is a gui too!

```{r,eval=FALSE}
biblioshiny()
```


> This concludes the example. There are various online sources to take this further


